Background Heart rate variability (HRV) provides information about the activity of the autonomic nervous system. Because of the small amount of data collected, the importance of HRV has not yet been proven in clinical practice. To collect population-level data, smartphone applications leveraging photoplethysmography (PPG) and some medical knowledge could provide the means for it. Objective To assess the capabilities of our smartphone application, we compared PPG (pulse rate variability (PRV)) with ECG (HRV). To have a baseline, we also compared the differences among ECG channels. Method We took fifty parallel measurements using iPhone 6 at a 240 Hz sampling frequency and Cardiax PC-ECG devices. The correspondence between the PRV and HRV indices was investigated using correlation, linear regression, and Bland-Altman analysis. Results High PPG accuracy: the deviation of PPG-ECG is comparable to that of ECG channels. Mean deviation between PPG-ECG and two ECG channels: RR: 0.01 ms–0.06 ms, SDNN: 0.78 ms–0.46 ms, RMSSD: 1.79 ms–1.21 ms, and pNN50: 2.43%–1.63%. Conclusions Our iPhone application yielded good results on PPG-based PRV indices compared to ECG-based HRV indices and to differences among ECG channels. We plan to extend our results on the PPG-ECG correspondence with a deeper analysis of the different ECG channels.
Abstract. For One-Class Classification problems several methods have been proposed in the literature. These methods all have the common feature that the decision boundary is learnt by just using a set of the positive examples. Here we propose a method that extends the training set with a counter-example set, which is generated directly using the set of positive examples. Using the extended training set, a binary classifier (here ν-SVM) is applied to separate the positive and the negative points. The results of this novel technique are compared with those of One-Class SVM and the Gaussian Mixture Model on several One-Class Classification tasks.
Abstract. In this paper we focus on the anti-phoneme modelling part of segment-based speech recognition, where we have to distinguish the real phonemes from anything else which may appear (like parts of phonemes, several consecutive phonemes and noise). As it has to be performed while only having samples of the correct phonemes, it is an example of one-class classification. To solve this problem, first all phonemes are modelled with a number of Gaussian distributions; then the problem is converted into a two-class classification task by generating counter-examples; this way some machine learning algorithm (like ANNs) can be used to separate the two classes. We tested two methods for a counter-example generation like this: one was a solution specific to the anti-phoneme problem, while the other used a general algorithm. By making modifications to the latter to reduce its time requirements, we were able to achieve an improvement in the recognition scores of over 60% compared to having no anti-phoneme model at all, and it performed considerably better than the other two methods.
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